{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost\n",
       "0              Budweiser       144      15      4.7  0.43\n",
       "1                Schlitz       151      19      4.9  0.43\n",
       "2              Lowenbrau       157      15      0.9  0.48\n",
       "3            Kronenbourg       170       7      5.2  0.73\n",
       "4               Heineken       152      11      5.0  0.77\n",
       "5          Old_Milwaukee       145      23      4.6  0.28\n",
       "6             Augsberger       175      24      5.5  0.40\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
       "8            Miller_Lite        99      10      4.3  0.43\n",
       "9        Budweiser_Light       113       8      3.7  0.40\n",
       "10                 Coors       140      18      4.6  0.44\n",
       "11           Coors_Light       102      15      4.1  0.46\n",
       "12        Michelob_Light       135      11      4.2  0.50\n",
       "13                 Becks       150      19      4.7  0.76\n",
       "14                 Kirin       149       6      5.0  0.79\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
       "16                 Hamms       139      19      4.4  0.43\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
       "19         Schlitz_Light        97       7      4.2  0.47"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# beer dataset\n",
    "import pandas as pd\n",
    "beer = pd.read_csv('data.txt', sep=' ')\n",
    "beer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = beer[[\"calories\",\"sodium\",\"alcohol\",\"cost\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km = KMeans(n_clusters=3).fit(X)\n",
    "km2 = KMeans(n_clusters=2).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 0, 1, 1, 0, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        0         1\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        0         1\n",
       "0              Budweiser       144      15      4.7  0.43        1         0\n",
       "1                Schlitz       151      19      4.9  0.43        1         0\n",
       "2              Lowenbrau       157      15      0.9  0.48        1         0\n",
       "3            Kronenbourg       170       7      5.2  0.73        1         0\n",
       "4               Heineken       152      11      5.0  0.77        1         0\n",
       "5          Old_Milwaukee       145      23      4.6  0.28        1         0\n",
       "6             Augsberger       175      24      5.5  0.40        1         0\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        1         0\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        1         0\n",
       "10                 Coors       140      18      4.6  0.44        1         0\n",
       "16                 Hamms       139      19      4.4  0.43        1         0\n",
       "12        Michelob_Light       135      11      4.2  0.50        1         0\n",
       "13                 Becks       150      19      4.7  0.76        1         0\n",
       "14                 Kirin       149       6      5.0  0.79        1         0\n",
       "9        Budweiser_Light       113       8      3.7  0.40        2         1\n",
       "8            Miller_Lite        99      10      4.3  0.43        2         1\n",
       "11           Coors_Light       102      15      4.1  0.46        2         1\n",
       "19         Schlitz_Light        97       7      4.2  0.47        2         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['cluster'] = km.labels_\n",
    "beer['cluster2'] = km2.labels_\n",
    "beer.sort_values('cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "%matplotlib inline\n",
    "\n",
    "cluster_centers = km.cluster_centers_\n",
    "\n",
    "cluster_centers_2 = km2.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70.00</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.075000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories  sodium   alcohol      cost  cluster2\n",
       "cluster                                                \n",
       "0           70.00    10.5  2.600000  0.420000         1\n",
       "1          150.00    17.0  4.521429  0.520714         0\n",
       "2          102.75    10.0  4.075000  0.440000         1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby(\"cluster\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.833333</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            calories     sodium   alcohol      cost   cluster\n",
       "cluster2                                                     \n",
       "0         150.000000  17.000000  4.521429  0.520714  1.000000\n",
       "1          91.833333  10.166667  3.583333  0.433333  1.333333"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby(\"cluster2\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = beer.groupby(\"cluster\").mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "colors = np.array(['red', 'green', 'blue', 'yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Alcohol')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(beer[\"calories\"], beer[\"alcohol\"],c=colors[beer[\"cluster\"]])\n",
    "\n",
    "plt.scatter(centers.calories, centers.alcohol, linewidths=3, marker='+', s=300, c='black')\n",
    "\n",
    "plt.xlabel(\"Calories\")\n",
    "plt.ylabel(\"Alcohol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\pytho3.6\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'With 3 centroids initialized')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(beer[[\"calories\",\"sodium\",\"alcohol\",\"cost\"]],s=100, alpha=1, c=colors[beer[\"cluster\"]], figsize=(10,10))\n",
    "plt.suptitle(\"With 3 centroids initialized\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\pytho3.6\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'With 2 centroids initialized')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(beer[[\"calories\",\"sodium\",\"alcohol\",\"cost\"]],s=100, alpha=1, c=colors[beer[\"cluster2\"]], figsize=(10,10))\n",
    "plt.suptitle(\"With 2 centroids initialized\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\pytho3.6\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "d:\\pytho3.6\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.38791334,  0.00779468,  0.43380786, -0.45682969],\n",
       "       [ 0.6250656 ,  0.63136906,  0.62241997, -0.45682969],\n",
       "       [ 0.82833896,  0.00779468, -3.14982226, -0.10269815],\n",
       "       [ 1.26876459, -1.23935408,  0.90533814,  1.66795955],\n",
       "       [ 0.65894449, -0.6157797 ,  0.71672602,  1.95126478],\n",
       "       [ 0.42179223,  1.25494344,  0.3395018 , -1.5192243 ],\n",
       "       [ 1.43815906,  1.41083704,  1.1882563 , -0.66930861],\n",
       "       [ 0.55730781,  1.87851782,  0.43380786, -0.52765599],\n",
       "       [-1.1366369 , -0.7716733 ,  0.05658363, -0.45682969],\n",
       "       [-0.66233238, -1.08346049, -0.5092527 , -0.66930861],\n",
       "       [ 0.25239776,  0.47547547,  0.3395018 , -0.38600338],\n",
       "       [-1.03500022,  0.00779468, -0.13202848, -0.24435076],\n",
       "       [ 0.08300329, -0.6157797 , -0.03772242,  0.03895447],\n",
       "       [ 0.59118671,  0.63136906,  0.43380786,  1.88043848],\n",
       "       [ 0.55730781, -1.39524768,  0.71672602,  2.0929174 ],\n",
       "       [-2.18688263,  0.00779468, -1.82953748, -0.81096123],\n",
       "       [ 0.21851887,  0.63136906,  0.15088969, -0.45682969],\n",
       "       [ 0.38791334,  1.41083704,  0.62241997, -0.45682969],\n",
       "       [-2.05136705, -1.39524768, -1.26370115, -0.24435076],\n",
       "       [-1.20439469, -1.23935408, -0.03772242, -0.17352445]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3).fit(X_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2  \\\n",
       "14                 Kirin       149       6      5.0  0.79        1         0   \n",
       "3            Kronenbourg       170       7      5.2  0.73        1         0   \n",
       "4               Heineken       152      11      5.0  0.77        1         0   \n",
       "13                 Becks       150      19      4.7  0.76        1         0   \n",
       "9        Budweiser_Light       113       8      3.7  0.40        2         1   \n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        0         1   \n",
       "12        Michelob_Light       135      11      4.2  0.50        1         0   \n",
       "11           Coors_Light       102      15      4.1  0.46        2         1   \n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        0         1   \n",
       "19         Schlitz_Light        97       7      4.2  0.47        2         1   \n",
       "2              Lowenbrau       157      15      0.9  0.48        1         0   \n",
       "8            Miller_Lite        99      10      4.3  0.43        2         1   \n",
       "10                 Coors       140      18      4.6  0.44        1         0   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        1         0   \n",
       "6             Augsberger       175      24      5.5  0.40        1         0   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28        1         0   \n",
       "1                Schlitz       151      19      4.9  0.43        1         0   \n",
       "16                 Hamms       139      19      4.4  0.43        1         0   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        1         0   \n",
       "0              Budweiser       144      15      4.7  0.43        1         0   \n",
       "\n",
       "    scaled_cluster  \n",
       "14               0  \n",
       "3                0  \n",
       "4                0  \n",
       "13               0  \n",
       "9                1  \n",
       "15               1  \n",
       "12               1  \n",
       "11               1  \n",
       "18               1  \n",
       "19               1  \n",
       "2                1  \n",
       "8                1  \n",
       "10               2  \n",
       "7                2  \n",
       "6                2  \n",
       "5                2  \n",
       "1                2  \n",
       "16               2  \n",
       "17               2  \n",
       "0                2  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer[\"scaled_cluster\"] = km.labels_\n",
    "beer.sort_values(\"scaled_cluster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the \"characteristics\" of each cluster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scaled_cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155.250</td>\n",
       "      <td>10.750</td>\n",
       "      <td>4.9750</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105.375</td>\n",
       "      <td>10.875</td>\n",
       "      <td>3.3250</td>\n",
       "      <td>0.4475</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>148.375</td>\n",
       "      <td>21.125</td>\n",
       "      <td>4.7875</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                calories  sodium  alcohol    cost  cluster  cluster2\n",
       "scaled_cluster                                                      \n",
       "0                155.250  10.750   4.9750  0.7625     1.00      0.00\n",
       "1                105.375  10.875   3.3250  0.4475     1.25      0.75\n",
       "2                148.375  21.125   4.7875  0.4075     1.00      0.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby(\"scaled_cluster\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\pytho3.6\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated, use pandas.plotting.scatter_matrix instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "      dtype=object)"
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(X, c=colors[beer.scaled_cluster], alpha=1, figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类评估：轮廓系数（Silhouette Coefficient ）\n",
    "\n",
    "<img src=\"1.png\" alt=\"FAO\" width=\"490\">\n",
    "\n",
    "- 计算样本i到同簇其他样本的平均距离ai。ai 越小，说明样本i越应该被聚类到该簇。将ai 称为样本i的簇内不相似度。\n",
    "- 计算样本i到其他某簇Cj 的所有样本的平均距离bij，称为样本i与簇Cj 的不相似度。定义为样本i的簇间不相似度：bi =min{bi1, bi2, ..., bik}\n",
    "\n",
    "\n",
    "* si接近1，则说明样本i聚类合理\n",
    "* si接近-1，则说明样本i更应该分类到另外的簇\n",
    "* 若si 近似为0，则说明样本i在两个簇的边界上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1797806808940007 0.6731775046455796\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "score_scaled = metrics.silhouette_score(X,beer.scaled_cluster)\n",
    "score = metrics.silhouette_score(X,beer.cluster)\n",
    "print(score_scaled, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6917656034079486,\n",
       " 0.6731775046455796,\n",
       " 0.5857040721127795,\n",
       " 0.4052740802143175,\n",
       " 0.39888288049162546,\n",
       " 0.43776116697963124,\n",
       " 0.38946337473125997,\n",
       " 0.39746405172426014,\n",
       " 0.3915697409245163,\n",
       " 0.41282646329875183,\n",
       " 0.3459775237127248,\n",
       " 0.31221439248428434,\n",
       " 0.30707782144770296,\n",
       " 0.31834561839139497,\n",
       " 0.2849514001174898,\n",
       " 0.23498077333071996,\n",
       " 0.1588091017496281,\n",
       " 0.08423051380151177]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = []\n",
    "for k in range(2,20):\n",
    "    labels = KMeans(n_clusters=k).fit(X).labels_\n",
    "    score = metrics.silhouette_score(X, labels)\n",
    "    scores.append(score)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Sihouette Score')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(range(2,20)), scores)\n",
    "plt.xlabel(\"Number of Clusters Initialized\")\n",
    "plt.ylabel(\"Sihouette Score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  DBSCAN clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "db = DBSCAN(eps=10, min_samples=2).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "      <th>cluster_db</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2  \\\n",
       "9        Budweiser_Light       113       8      3.7  0.40        2         1   \n",
       "3            Kronenbourg       170       7      5.2  0.73        1         0   \n",
       "6             Augsberger       175      24      5.5  0.40        1         0   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        1         0   \n",
       "16                 Hamms       139      19      4.4  0.43        1         0   \n",
       "14                 Kirin       149       6      5.0  0.79        1         0   \n",
       "13                 Becks       150      19      4.7  0.76        1         0   \n",
       "12        Michelob_Light       135      11      4.2  0.50        1         0   \n",
       "10                 Coors       140      18      4.6  0.44        1         0   \n",
       "0              Budweiser       144      15      4.7  0.43        1         0   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        1         0   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28        1         0   \n",
       "4               Heineken       152      11      5.0  0.77        1         0   \n",
       "2              Lowenbrau       157      15      0.9  0.48        1         0   \n",
       "1                Schlitz       151      19      4.9  0.43        1         0   \n",
       "8            Miller_Lite        99      10      4.3  0.43        2         1   \n",
       "11           Coors_Light       102      15      4.1  0.46        2         1   \n",
       "19         Schlitz_Light        97       7      4.2  0.47        2         1   \n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        0         1   \n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        0         1   \n",
       "\n",
       "    scaled_cluster  cluster_db  \n",
       "9                1          -1  \n",
       "3                0          -1  \n",
       "6                2          -1  \n",
       "17               2           0  \n",
       "16               2           0  \n",
       "14               0           0  \n",
       "13               0           0  \n",
       "12               1           0  \n",
       "10               2           0  \n",
       "0                2           0  \n",
       "7                2           0  \n",
       "5                2           0  \n",
       "4                0           0  \n",
       "2                1           0  \n",
       "1                2           0  \n",
       "8                1           1  \n",
       "11               1           1  \n",
       "19               1           1  \n",
       "15               1           2  \n",
       "18               1           2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['cluster_db'] = labels\n",
    "beer.sort_values('cluster_db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_db</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>152.666667</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>146.250000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>4.383333</td>\n",
       "      <td>0.513333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.333333</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>0.453333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              calories     sodium   alcohol      cost   cluster  cluster2  \\\n",
       "cluster_db                                                                  \n",
       "-1          152.666667  13.000000  4.800000  0.510000  1.333333  0.333333   \n",
       " 0          146.250000  17.250000  4.383333  0.513333  1.000000  0.000000   \n",
       " 1           99.333333  10.666667  4.200000  0.453333  2.000000  1.000000   \n",
       " 2           70.000000  10.500000  2.600000  0.420000  0.000000  1.000000   \n",
       "\n",
       "            scaled_cluster  \n",
       "cluster_db                  \n",
       "-1                1.000000  \n",
       " 0                1.333333  \n",
       " 1                1.000000  \n",
       " 2                1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby('cluster_db').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\pytho3.6\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated, use pandas.plotting.scatter_matrix instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000C890FD5438>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(X, c=colors[beer.cluster_db], figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.06781609566358748,\n",
       " 0.1626084889128696,\n",
       " 0.12626205982196476,\n",
       " 0.16564759416041527,\n",
       " 0.42951251219183106,\n",
       " 0.49530955296776086,\n",
       " 0.49530955296776086,\n",
       " 0.49530955296776086,\n",
       " 0.49530955296776086,\n",
       " 0.5857040721127795,\n",
       " 0.5857040721127795,\n",
       " 0.5238781710613801,\n",
       " 0.5238781710613801,\n",
       " 0.6731775046455796,\n",
       " 0.6731775046455796]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = []\n",
    "for eps in range(5,20):\n",
    "    labels = DBSCAN(eps=eps, min_samples=2).fit(X).labels_\n",
    "    score = metrics.silhouette_score(X, labels)\n",
    "#     score_list = [eps,2,score]\n",
    "    scores.append(score)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Sihouette Score')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(range(5,20)), scores)\n",
    "plt.xlabel(\"Number of Clusters Initialized\")\n",
    "plt.ylabel(\"Sihouette Score\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
